Lecture 2
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Lecture 2. Information Retrieval and Text Mining. Recap of the previous lecture. Basic inverted indexes: Structure: Dictionary and Postings Key step in construction: Sorting Boolean query processing Simple optimization Linear time merging Overview of course topics. Plan for this lecture.

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Lecture 2

Lecture 2

Information Retrieval and Text Mining


Recap of the previous lecture

Recap of the previous lecture

Basic inverted indexes:

Structure: Dictionary and Postings

Key step in construction: Sorting

Boolean query processing

Simple optimization

Linear time merging

Overview of course topics


Plan for this lecture

Plan for this lecture

Finish basic indexing

Tokenization

What terms do we put in the index?

Query processing – speedups

Proximity/phrase queries


Recall basic indexing pipeline

Recall basic indexing pipeline

Tokenizer

Friends

Romans

Countrymen

Token stream.

Linguistic modules

friend

roman

countryman

Modified tokens.

2

4

Indexer

friend

1

2

roman

Inverted index.

16

13

countryman

Documents to

be indexed.

Friends, Romans, countrymen.


Parsing a document

Parsing a document

What format is it in?

pdf/word/excel/html?

What language is it in?

What character set is in use?

Each of these is a classification problem, which we will study later in the course.

But there are complications …


Format language stripping

Format/language stripping

Documents being indexed can include docs from many different languages

A single index may have to contain terms of several languages.

Sometimes a document or its components can contain multiple languages/formats

French email with a Portuguese pdf attachment.

What is a unit document?

An email?

With attachments?

An email with a zip containing documents?


Tokenization

Tokenization


Tokenization1

Tokenization

Input: “Friends, Romans and Countrymen”

Output: Tokens

Friends

Romans

Countrymen

Each such token is now a candidate for an index entry, after further processing

Described below

But what are valid tokens to emit?


Tokenization2

Tokenization

Issues in tokenization:

Finland’s capital 

Finland? Finlands? Finland’s?

Hewlett-PackardHewlett and Packard as two tokens?

State-of-the-art: break up hyphenated sequence.

co-education ?

the hold-him-back-and-drag-him-away-maneuver ?

San Francisco: one token or two? How do you decide it is one token?


Numbers

Numbers

3/12/91

Mar. 12, 1991

55 B.C.

B-52

My PGP key is 324a3df234cb23e

100.2.86.144

Generally, don’t index as text.

Will often index “meta-data” separately

Creation date, format, etc.


Tokenization language issues

Tokenization: Language issues

L'ensemble one token or two?

L ? L’ ? Le ?

Want ensemble to match with un ensemble

German noun compounds are not segmented

Lebensversicherungsgesellschaftsangestellter

‘life insurance company employee’


Tokenization language issues1

Tokenization: language issues

Chinese and Japanese have no spaces between words:

Not always guaranteed a unique tokenization

Further complicated in Japanese, with multiple alphabets intermingled

Dates/amounts in multiple formats

Katakana

Hiragana

Kanji

“Romaji”

フォーチュン500社は情報不足のため時間あた$500K(約6,000万円)

End-user can express query entirely in hiragana!


Tokenization language issues2

Tokenization: language issues

Arabic (or Hebrew) is basically written right to left, but with certain items like numbers written left to right

Words are separated, but letter forms within a word form complex ligatures

استقلت الجزائر في سنة 1962 بعد 132 عاما من الاحتلال الفرنسي.

← → ← → ← start

‘Algeria achieved its independence in 1962 after 132 years of French occupation.’

With Unicode, the surface presentation is complex, but the stored form is straightforward


Normalization

Normalization

Need to “normalize” terms in indexed text as well as query terms into the same form

We want to match U.S.A. and USA

We most commonly implicitly define equivalence classes of terms

e.g., by deleting periods in a term

Alternative is to do limited expansion:

Enter: windowSearch: window, windows

Enter: windowsSearch: Windows, windows

Enter: WindowsSearch: Windows

Potentially more powerful, but less efficient


Case folding

Case folding

Reduce all letters to lower case

exception: upper case (in mid-sentence?)

e.g., General Motors

Fed vs. fed

SAIL vs. sail

Often best to lower case everything, since users will use lowercase regardless of ‘correct’ capitalization


Normalizing punctuation

Normalizing Punctuation

Ne’er vs. never: use language-specific, handcrafted “locale” to normalize.

Which language?

Most common: detect/apply language at a pre-determined granularity: doc/paragraph.

U.S.A. vs. USA – remove all periods or use locale.

a.out


Thesauri and soundex

Thesauri and soundex

Handle synonyms and homonyms

Hand-constructed equivalence classes

e.g., car = automobile

color = colour

Rewrite to form equivalence classes

Index such equivalences

When the document contains automobile, index it under car as well (usually, also vice-versa)

Or expand query?

When the query contains automobile, look under car as well


Soundex

Soundex

Traditional class of heuristics to expand a query into phonetic equivalents

Language specific – mainly for names

E.g., chebyshevtchebycheff

More on this later ...


Lemmatization

Lemmatization

Reduce inflectional/variant forms to base form

E.g.,

am, are,is be

car, cars, car's, cars'car

the boy's cars are different colorsthe boy car be different color

Lemmatization implies doing “proper” reduction to dictionary headword form


Stemming

Stemming

Reduce terms to their “roots” before indexing

“Stemming” suggests crude affix chopping

language dependent

e.g., automate(s), automatic, automation all reduced to automat.

for exampl compress and

compress ar both accept

as equival to compress

for example compressed

and compression are both

accepted as equivalent to

compress.


Porter s algorithm

Porter’s algorithm

Commonest algorithm for stemming English

Results suggest at least as good as other stemming options

Conventions + 5 phases of reductions

phases applied sequentially

each phase consists of a set of commands

sample convention: Of the rules in a compound command, select the one that applies to the longest suffix.


Typical rules in porter

Typical rules in Porter

ssesss

iesi

ationalate

tionaltion

(m>1) EMENT →

replacement → replac

cement → cement


Other stemmers

Other stemmers

Other stemmers exist, e.g., Lovins stemmer http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm

Single-pass, longest suffix removal (about 250 rules)

Motivated by Linguistics as well as IR

Full morphological analysis – at most modest benefits for retrieval (in English)

Do stemming and other normalizations help?

Often very mixed results: really help recall for some queries but harm precision on others


Language specificity

Language-specificity

Many of the above features embody transformations that are

Language-specific and

Often, application-specific

These are “plug-in” addenda to the indexing process

Both open source and commercial plug-ins available for handling these


Normalization other languages

Normalization: other languages

Accents: résumé vs. resume.

Most important criterion:

How are your users likely to write their queries for these words?

Even in languages that standardly have accents, users often may not type them

German: Tuebingen vs. Tübingen

Should be equivalent


Normalization other languages1

Normalization: other languages

Need to “normalize” indexed text as well as query terms into the same form

Character-level alphabet detection and conversion

Tokenization not separable from this.

Sometimes ambiguous:

7月30日 vs. 7/30

Is this

German “mit”?

Morgen gehe ich zum MIT …


Dictionary entries first cut

Dictionary entries – first cut

ensemble.french

時間.japanese

MIT.english

mit.german

guaranteed.english

entries.english

sometimes.english

tokenization.english

These may be grouped by language. More on this in ranking/query processing.


Faster postings merges skip pointers

Faster postings merges:Skip pointers


Recall basic merge

Recall basic merge

Walk through the two postings simultaneously, in time linear in the total number of postings entries

2

4

8

16

32

64

Brutus

8

Caesar

1

2

3

5

8

17

21

128

2

31

If the list lengths are m and n, the merge takes O(m+n)

operations.

Can we do better?

Yes, if index isn’t changing too fast.


Augment postings with skip pointers at indexing time

Augment postings with skip pointers (at indexing time)

2

4

8

16

32

64

128

1

2

3

5

8

17

21

31

128

16

31

8

  • Why?

  • To skip postings that will not figure in the search results.

  • How?

  • Where do we place skip pointers?


Query processing with skip pointers

Query processing with skip pointers

2

4

8

16

32

64

128

1

2

3

5

8

17

21

31

When we get to 16 on the top list, we see that its

successor is 32.

But the skip successor of 8 on the lower list is 31, so

we can skip ahead past the intervening postings.

128

16

31

8

Suppose we’ve stepped through the lists until we process 8 on each list.


Skip pointers

Skip pointers

  • Tradeoff?


Where do we place skips

Where do we place skips?

Tradeoff:

More skips  shorter skip spans  more likely to skip. But lots of comparisons to skip pointers.

Fewer skips  few pointer comparison, but then long skip spans  few successful skips.


Placing skips

Placing skips

Simple heuristic: for postings of length L, use L evenly-spaced skip pointers.

This ignores the distribution of query terms.

Easy if the index is relatively static; harder if L keeps changing because of updates.

This definitely used to help; with modern hardware it may not (Bahle et al. 2002)

The cost of loading a bigger postings list outweighs the gain from quicker in memory merging


Phrase queries

Phrase queries


Phrase queries1

Phrase queries

Want to answer queries such as “stanford university” – as a phrase

Thus the sentence “I went to university at Stanford” is not a match.

The concept of phrase queries has proven easily understood by users; about 10% of web queries are phrase queries

No longer suffices to store only

<term : docs> entries


A first attempt biword indexes

A first attempt: Biword indexes

Index every consecutive pair of terms in the text as a phrase

For example the text “Friends, Romans, Countrymen” would generate the biwords

friends romans

romans countrymen

Each of these biwords is now a dictionary term

Two-word phrase query-processing is now immediate.


Longer phrase queries

Longer phrase queries

Longer phrases are processed as we did with wild-cards:

stanford university palo alto can be broken into the Boolean query on biwords:

stanford university AND university palo AND palo alto

Without the docs, we cannot verify that the docs matching the above Boolean query do contain the phrase.

Can have false positives!


Extended biwords

Extended biwords

Parse the indexed text and perform part-of-speech-tagging (POST).

Bucket the terms into (say) Nouns (N) and articles/prepositions (X).

Now deem any string of terms of the form NX*N to be an extended biword.

Each such extended biword is now made a term in the dictionary.

Example: catcher in the rye

N X X N

Query processing: parse it into N’s and X’s

Segment query into enhanced biwords

Look up index


Issues for biword indexes

Issues for biword indexes

False positives, as noted before

Index blowup due to bigger dictionary

For extended biword index, parsing longer queries into conjunctions:

E.g., the query tangerine trees and marmalade skies is parsed into

tangerine trees AND trees and marmalade AND marmalade skies

Not standard solution (for all biwords)


Solution 2 positional indexes

Solution 2: Positional indexes

Store, for each term, entries of the form:

<number of docs containing term;

doc1: position1, position2 … ;

doc2: position1, position2 … ;

etc.>


Positional index example

Positional index example

Can compress position values/offsets

Nevertheless, this expands postings storage substantially

<be: 993427;

1: 7, 18, 33, 72, 86, 231;

2: 3, 149;

4: 17, 191, 291, 430, 434;

5: 363, 367, …>

Which of docs 1,2,4,5

could contain “to be

or not to be”?


Processing a phrase query

Processing a phrase query

Extract inverted index entries for each distinct term: to, be, or, not.

Merge their doc:position lists to enumerate all positions with “to be or not to be”.

to:

2:1,17,74,222,551;4:8,16,190,429,433;7:13,23,191; ...

be:

1:17,19; 4:17,191,291,430,434;5:14,19,101; ...

Same general method for proximity searches


Proximity queries

Proximity queries

LIMIT! /3 STATUTE /3 FEDERAL /2 TORT Here, /k means “within k words of”.

Clearly, positional indexes can be used for such queries; biword indexes cannot.

Exercise: Adapt the linear merge of postings to handle proximity queries. Can you make it work for any value of k?


Positional index size

Positional index size

Can compress position values/offsets as we did with docs in the last lecture

Nevertheless, this expands postings storage substantially


Positional index size1

Positional index size

Need an entry for each occurrence, not just once per document

Index size depends on average document size

Average web page has <1000 terms

SEC filings, books, even some epic poems … easily 100,000 terms

Consider a term with frequency 0.1%

Document size

Postings

Positional postings

1000

1

1

100,000

1

100

Why?


Rules of thumb

Rules of thumb

A positional index is 2-4 as large as a non-positional index

Positional index size 35-50% of volume of original text

Caveat: all of this holds for “English-like” languages


Combination schemes

Combination schemes

These two approaches can be profitably combined

For particular phrases (“Michael Jackson”, “Britney Spears”) it is inefficient to keep on merging positional postings lists

Even more so for phrases like “The Who”

Williams et al. (2004) evaluate a more sophisticated mixed indexing scheme

A typical web query mixture was executed in ¼ of the time of using just a positional index

It required 26% more space than having a positional index alone


Resources for today s lecture

Resources for today’s lecture

MG 3.6, 4.3; MIR 7.2

Porter’s stemmer: http://www.tartarus.org/~martin/PorterStemmer/

H.E. Williams, J. Zobel, and D. Bahle. 2004. “Fast Phrase Querying with Combined Indexes”, ACM Transactions on Information Systems.

http://www.seg.rmit.edu.au/research/research.php?author=4

D. Bahle, H. Williams, and J. Zobel. Efficient phrase querying with an auxiliary index. SIGIR 2002, pp. 215-221.


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